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Fast and Robust Filtering-Based Image Magnification

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Image Analysis and Recognition (ICIAR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4141))

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Abstract

Image magnification, or interpolation, produces a high resolution image from a low resolution, and perhaps noisy image. There have been proposed a variety of magnification algorithms. However, they are either sensitive to the noise, or non-robust to the blocking artifacts, or of high computational complexity, which hence limits their utility. In this paper, we propose an alternative magnification approach utilizing a filtering-based implementation scheme and novel regularization through coupling bilateral filtering with the digital total variation model. The approach is simple, fast, and robust to both the noise and blocking artifacts. Experiment results demonstrate the effectiveness of our approach.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China

    Wenze Shao

  2. Graduate School, Nanjing University of Science and Technology, 210094, Nanjing, China

    Zhihui Wei

Authors
  1. Wenze Shao
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  2. Zhihui Wei
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Editor information

Editors and Affiliations

  1. Departamento de Engenharia Electrotécnica e de Computadores da, Faculdade de Engenharia da, Universidade do Porto, Campus FEUP, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed S. Kamel

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© 2006 Springer-Verlag Berlin Heidelberg

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Shao, W., Wei, Z. (2006). Fast and Robust Filtering-Based Image Magnification. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_5

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  • DOI: https://doi.org/10.1007/11867586_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44891-4

  • Online ISBN: 978-3-540-44893-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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